Through numerous interactions with computer systems in their day-to-day lives, users are exposed to a wide array of content, such communications (e.g., video, instant messaging, or interacting with a virtual agent) and various forms of digital media (e.g., internet sites, television shows, movies, augmented reality, and/or interactive computer games). Throughout these many interactions, affective computing systems can measure a user's affective response to the content and analyze the information. This analysis may provide insight that can be used to improve the current and/or future content presented to the user. For example, a computer game may be able to recognize if the difficulty level of the game is appropriate for the user, and adapt the game accordingly. In another example, a television may learn what programs a user is likely to enjoy from monitoring the user's reaction to previously shown programs. The television may then be able to select and deliver more appropriate content to the user, with essentially no need for the user to actively choose or intervene.
Advances in human-computer interaction have led to the development of multiple devices that may be utilized to measure a person's affective response. For example, systems like Microsoft Kinect (tm) that involve inexpensive cameras and advanced analysis methods are able to track a user's movement, allowing the user to interact with computers using gestures. Similarly, eye tracking is another technology that is finding its way into more and more computing. Tracking a user's gaze with one or more cameras enables computer systems to detect what content or objects the user is paying attention to. Other technologies found in an increasing number of consumer applications include various sensors that can measure physiological signals such as heart rate, blood-volume pulse, galvanic skin response (GSR), skin temperature, respiration, or brainwave activity such as electroencephalography (EEG).
Often, the devices used to measure users are mobile and/or wireless (e.g., bracelets with GSR sensors, cameras, headsets with EEG sensors or implants); thus, they tend to required batteries for power. However, taking the multitude of measurements of a user's affective response, which affective computing application often demands, may consume a lot of power that may drain the sensors' batteries. Unfortunately, the need to supply sufficient power for operating sensors and/or analyzing the data they produce is currently met by unsatisfactory means. For example, the size of the battery powering a sensor may be increased, but this is undesirable since it makes the mobile devices more cumbersome and less comfortable for the user. Another inconvenient possibility is to recharge power sources more often to meet the power demands. Yet another possibility is to measure users less or decrease the quality of the measurements, thus reducing the sensors' power demands. However, this too is problematic, since it means that the computer systems may reduce the quality of service provided.
Thus, there is a need to reduce the power consumption of devices used to measure affective response, and to do so without severely inconveniencing users or dramatically reducing the quality of data provided by the devices.